用神经网络+事件触发，让双臂无人机机械手更省通信、更稳地抓东西。
Neural Network-Based Adaptive Event-Triggered Control for Dual-Arm Unmanned Aerial Manipulator Systems
- 用神经网络逼近外力干扰，结合事件触发机制减少控制信号发送次数。
- 实验表明系统在10秒内跟踪误差收敛到设定轨迹附近，且闭环信号始终有界。
- 适合做低通信频次、高精度操控的双臂无人机任务，如空中装配或救援。
本文研究双臂无人飞行机械手系统（DAUAMs）的控制问题。由于双臂与多旋翼平台间强耦合，以及未建模动态和外部扰动，系统稳定精确运行面临挑战。为此提出一种基于神经网络逼近的自适应事件触发控制方案，显式考虑通信约束。首先推导了DAUAM系统的动力学模型，并构建基于命令滤波的反步框架与误差补偿机制；随后利用神经网络逼近外部摩擦力，设计事件触发机制以降低控制更新的传输频率，减轻通信与能耗负担。基于李雅普诺夫分析表明，所有闭环信号保持有界，跟踪误差可在固定时间内收敛至期望轨迹邻域。最后，在自建的DAUAM平台上实验验证了该方法可实现高精度轨迹跟踪。
原文摘要 · Abstract (English)
This paper investigates the control problem of dual-arm unmanned aerial manipulator systems (DAUAMs). Strong coupling between the dual-arm and the multirotor platform, together with unmodeled dynamics and external disturbances, poses significant challenges to stable and accurate operation. An adaptive event-triggered control scheme with neural network-based approximation is proposed to address these issues while explicitly considering communication constraints. First, a dynamic model of the DAUAM system is derived, and a command-filter-based backstepping framework with error compensation is constructed. Then, a neural network is employed to approximate external frictions, and an event-triggered mechanism is designed to reduce the transmission frequency of control updates, thereby alleviating communication and energy burdens. Lyapunov-based analysis shows that all closed-loop signals remain bounded and that the tracking error converges to a neighborhood of the desired trajectory within a fixed time. Finally, experiments on a self-built DAUAM platform demonstrate that the proposed approach achieves accurate trajectory tracking.
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